dc.contributor.author | Arruti Illarramendi, Andoni | |
dc.contributor.author | Mendialdua Beitia, Iñigo | |
dc.contributor.author | Sierra Araujo, Basilio | |
dc.contributor.author | Lazkano Ortega, Elena | |
dc.contributor.author | Jauregi Iztueta, Ekaitz | |
dc.date.accessioned | 2024-01-11T15:13:39Z | |
dc.date.available | 2024-01-11T15:13:39Z | |
dc.date.issued | 2014-04-19 | |
dc.identifier.citation | Expert systems with applications 41(14) : 6251-6260 (2014) | es_ES |
dc.identifier.issn | 0957-4174 | |
dc.identifier.uri | http://hdl.handle.net/10810/63881 | |
dc.description.abstract | Binarization strategies decompose the original multi-class dataset into multiple two-class subsets, learning a different binary model for each new
subset. One-vs-All (OVA) and One-vs-One (OVO) are two of the most well-known techniques: One-vs-One separates a pair of classes in each binary sub-problem,
ignoring the remaining ones; and One-vs-All distinguishes one class from all the other classes. In this paper, we present two new OVA and OVO combinations where the best base classifier is applied in each sub-problem. The first method is called OVA+OVO since it combines the outputs obtained by OVA and OVO decomposition strategies. The second combination is named $New \: One \: Versus^{All}_{One}$ (NOV@), and its objective is to solve the problems found in OVA when different base classifiers are used in each sub-problem. In order to validate the performance of the new proposal, an empirical study has been carried out where the two new methods are compared with other well-known decomposition strategies from the literature. Experimental results show that both methods obtain promising results, especially NOV@. | es_ES |
dc.description.sponsorship | The work described in this paper was partially conducted within the Basque Government Research Team grant and the University of the Basque Country UPV/EHU and under grant UFI11/45 (BAILab). I. Mendialdua holds a grant from Basque Government. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | decomposition strategies | es_ES |
dc.subject | one against one | es_ES |
dc.subject | one against all | es_ES |
dc.title | NewOneVersusOneAll method: NOV@ | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2014 Elsevier Ltd. under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/abs/pii/S095741741400205X | es_ES |
dc.identifier.doi | 10.1016/j.eswa.2014.04.010 | |
dc.departamentoes | Arquitectura y Tecnología de Computadores | es_ES |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputagailuen Arkitektura eta Teknologia | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |